Heavy Rainfall Forecast Research Articles

Application Research of Wind Profile Radar in Short-term Heavy Rainfall Forecast of Typhoon in Fujian Province

Application Research of Wind Profile Radar in Short-term Heavy Rainfall Forecast of Typhoon in Fujian Province

Analysis of a Rainstorm Process in Nanjing Based on Multi-Source Observational Data and Lagrangian Method

Using multi-source observation data including automatic stations, radar, satellite, new detection equipment, and the Fifth Generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA-5) data, along with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) platform, an analysis was conducted on a rainstorm process that occurred in Nanjing on 15 June 2020, with the aim of providing reference for future urban flood control planning and heavy rainfall forecasting and early warning. The results showed that this rainstorm process was generated under the background of an eastward-moving northeast cold vortex and a southward retreat of the Western Pacific Subtropical High. Intense precipitation occurred near the region of large top brightness temperature (TBB) gradient values or the center of low TBB values on the northern side of the convective cloud cluster. During the heavy precipitation period, the differential propagation phase shift rate (KDP), differential reflectivity factor (ZDR), and zero-lag correlation coefficient (ρHV) detected by the S-band dual-polarization radar all increased significantly. The vertical structure of the wind field detected by the wind profile radar provided a good indication of changes in precipitation intensity, showing a strong correspondence between the timing of maximum precipitation and the intrusion of upper-level cold air. The abrupt increase in the integrated liquid water content observed by the microwave radiometer can serve as an important indicator of the onset of stronger precipitation. During the Meiyu season in Nanjing, convective precipitation was mainly composed of small to medium raindrops with diameters less than 3 mm, with falling velocities of raindrops mainly clustering between 2 and 6 m·s−1. The rainstorm process featured four water vapor transport channels: the mid-latitude westerly channel, the Indian Ocean channel, the South China Sea channel, and the Pacific Ocean channel. During heavy rainfall, the Pacific Ocean water vapor channel was the main channel at the middle and lower levels, while the South China Sea water vapor channel was the main channel at the upper level, both accounting for a trajectory proportion of 34.2%.

Improving Forecasts of the “21⋅7” Henan Extreme Rainfall Event Using a Radar Assimilation Scheme that Considers Hydrometeor Background Error Covariance

Abstract On 20–21 July 2021, a record-breaking rainfall event occurred in Henan Province, China, and a maximum hourly accumulated precipitation of 201.9 mm was recorded at Zhengzhou Meteorological Station. To improve the prediction of such extreme rainfall and to better understand the impacts of the radar reflectivity assimilation on forecasting, we assimilated radar reflectivity data using the hydrometeor background error covariance (HBEC) that includes vertical and multivariate correlations and then diagnosed the dynamic, thermal, and microphysical forecasts of this event. The results show that the radar reflectivity assimilation based on the HBEC properly transferred the observed radar reflectivity to the analysis of hydrometeors and other model states, and clearly improved the heavy rainfall forecast. The diagnosis of the dynamic and thermal forecasts indicated that the reflectivity assimilation based on the HBEC improved the convective environments of the precipitation systems, with stronger cold pools near the surface and deeper and wetter updrafts near Zhengzhou station, when compared with the experiment that did not assimilate radar reflectivity and the experiment that assimilated radar reflectivity without using the HBEC. The diagnosis of the microphysical forecasts further shows that assimilating reflectivity data using HBEC contributed to higher conversion rates of water vapor and cloud water to graupel and higher conversion rates of graupel and cloud water to rainwater, when compared with the other experiments. These improvements of both convective environments and microphysical processes within the convections ultimately enhanced the forecasts of this extreme rainfall event.

SDIPPWV: A novel hybrid prediction model based on stepwise decomposition-integration-prediction avoids future information leakage to predict precipitable water vapor from GNSS observations

Water vapor is an important meteorological parameter. Accurate prediction of water vapor content can be used to provide important reference information for heavy rainfall forecast and artificial precipitation operation. The current water vapor hybrid prediction model has the problem of future data leakage, and the error is accumulated by reconstructing the subsequence after prediction. Therefore, this paper proposes a stepwise decomposition-integration-prediction precipitable water vapor mechanism, named SDIPPWV, which can effectively solve the above problems. Firstly, High-precision precipitable water vapor (PWV) sequence is retrieved from Global Navigation Satellite System (GNSS) observation files. Then stepwise decomposition process uses a fixed-size window to segment the PWV sequence and Seasonal-Trend decomposition based on Loess (STL) to decompose the sequences within the window. Next, the features of the three sub-sequences are integrated to construct the feature space. Finally the prediction of PWV is obtained using 1D Convolutional Neural Network-Bidirectional Long Short Term Memory (1D CNN-BiLSTM). The model performance is verified using observation data from eight GNSS stations. The performance of the PWV prediction model proposed in this paper is effectively improved compared with the single prediction models and other hybrid models. The mean root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) of the eight stations are 0.2146 mm, 0.1132 mm, 1.29 %, and 0.9998, respectively. The results show that the model proposed in this paper improves the prediction accuracy of water vapor content while solving the data leakage problem.

Spatiotemporal Convolutional Approach for the Short-Term Forecast of Hourly Heavy Rainfall Probability Integrating Numerical Weather Predictions and Surface Observations

Abstract The accurate prediction of short-term rainfall, and in particular the forecast of hourly heavy rainfall (HHR) probability, remains challenging for numerical weather prediction (NWP) models. Here, we introduce a deep learning (DL) model, PredRNNv2-AWS, a convolutional recurrent neural network designed for deterministic short-term rainfall forecasting. This model integrates surface rainfall observations and atmospheric variables simulated by the Precision Weather Analysis and Forecasting System (PWAFS). Our DL model produces realistic hourly rainfall forecasts for the next 13 h. Quantitative evaluations show that the use of surface rainfall observations as one of the predictors achieves higher performance (threat score) with 263% and 186% relative improvements over NWP simulations for the first 3 h and the entire forecast hours, respectively, at a threshold of 5 mm h−1. Noting that the optical-flow method also performs well in the initial hours, its predictions quickly worsen in the final hours compared to other experiments. The machine learning model, LightGBM, is then integrated to classify HHR from the predicted hourly rainfall of PredRNNv2-AWS. The results show that PredRNNv2-AWS can better reflect actual HHR conditions compared with PredRNNv2 and PWAFS. A representative case demonstrates the superiority of PredRNNv2-AWS in predicting the evolution of the rainy system, which substantially improves the accuracy of the HHR prediction. A test case involving the extreme flood event in Zhengzhou exemplifies the generalizability of our proposed model. Our model offers a reliable framework to predict target variables that can be obtained from numerical simulations and observations, e.g., visibility, wind power, solar energy, and air pollution.

Improving Afternoon Thunderstorm Prediction over Complex Terrain with the Assimilation of Dense Ground-Based Observations: Four Cases in the Taipei Basin

Abstract In this study, we investigate the impact of assimilating densely distributed Global Navigation Satellite System (GNSS) zenith total delay (ZTD) and surface station (SFC) data on the prediction of very short-term heavy rainfall associated with afternoon thunderstorm (AT) events in the Taipei basin. Under weak synoptic-scale conditions, four cases characterized by different rainfall features are chosen for investigation. Experiments are conducted with a 3-h assimilation period, followed by 3-h forecasts. Also, various experiments are performed to explore the sensitivity of AT initialization. Data assimilation experiments are conducted with a convective-scale Weather Research and Forecasting–local ensemble transform Kalman filter (WRF-LETKF) system. The results show that ZTD assimilation can provide effective moisture corrections. Assimilating SFC wind and temperature data could additionally improve the near-surface convergence and cold bias, further increasing the impact of ZTD assimilation. Frequently assimilating SFC data every 10 min provides the best forecast performance especially for rainfall intensity predictions. Such a benefit could still be identified in the earlier forecast initialized 2 h before the start of the event. Detailed analysis of a case on 22 July 2019 reveals that frequent assimilation provides initial conditions that can lead to fast vertical expansion of the convection and trigger an intense AT. This study proposes a new metric using the fraction skill score to construct an informative diagram to evaluate the location and intensity of heavy rainfall forecast and display a clear characteristic of different cases. Issues of how assimilation strategies affect the impact of ground-based observations in a convective ensemble data assimilation system and AT development are also discussed. Significance Statement In this study, we investigate the impact of frequently assimilating densely distributed ground-based observations on predicting four afternoon thunderstorm events in the Taipei basin. While assimilating GNSS-ZTD data can improve the moisture fields for initializing convection, assimilating surface station data improves the prediction of rainfall location and intensity, particularly when surface data are assimilated at a very high frequency of 10 min.

Coupling Physical Factors for Precipitation Forecast in China With Graph Neural Network

AbstractPrecipitation exerts far‐reaching impacts on both socio‐economic fabric and individual well‐being, necessitating concerted efforts in accurate forecasting. Deep learning (DL) models have increasingly demonstrated their prowess in forecasting meteorological elements. However, traditional DL prediction models often grapple with heavy rainfall forecasting. In this study, we propose physics‐informed localized graph neural network models called ω‐GNN and ω‐EGNN, constrained by the coupling of physical variables and climatological background to predict precipitation in China. These models exhibit notable and robust improvements in identifying heavy rainfall while maintaining excellent performance in forecasting light rain by comparing to numerical weather prediction (NWP) and other DL models with multiple perturbation experiments in different data sets. Surprisingly, within a certain range, even when a DL model utilizes more input variables, GNN can still maintain its advantage. The methods to fuse physics into DL model demonstrated in this study may be promising and call for future studies.

Different Initial Condition Perturbation Methods for Convection-Permitting Ensemble Forecasting over South China during the Rainy Season

Abstract In this study, downscaling, ensemble data assimilation, time lagging, and their combination were used to generate initial condition (IC) perturbations for 12-h convection-permitting ensemble forecasting for heavy-rainfall events over South China during the rainy season in 2013–20. These events were classified as weak- and strong-forcing cases based on synoptic-scale forcing during the presummer rainy season and as landfalling tropical cyclone (TC) cases. This study investigated the impacts of various IC perturbation methods on multiscale characteristics of perturbations and the forecast performance for both nonprecipitation and precipitation variables. These perturbation methods represented different source IC uncertainties and thus differed in multiscale characteristics of perturbations in vertical structures, horizontal distributions, and time evolution. The combination of various IC perturbation methods evidently increased perturbations or spreads of precipitation in both magnitude and location and thus improved the forecast-error estimation. Such an improvement was most and least evident for TC cases during the early and late forecasts, respectively, and was more evident for strong- than weak-forcing cases beyond 6 h. The combination of various IC perturbation methods generally improved both the ensemble-mean and probabilistic forecasts with case-dependent improvements. For heavy rainfall forecasting, 1–6-h improvements were most prominent for TC cases in terms of discrimination and accuracy, while 7–12-h improvements were least prominent for weak-forcing cases in terms of reliability and accuracy. In particular, the improvements in predicting weak-forcing cases increased with spatial errors. In contrast, for strong-forcing cases, the improvements were least and most prominent before and beyond 6 h, respectively. Significance Statement Precipitation forecasting for heavy-rainfall events over South China in the rainy season is still challenging due to large uncertainties. Convection-permitting ensemble forecasting is expected to address such uncertainties to improve forecasts of heavy rainfall. However, it is not yet clear how to optimally design convection-permitting ensembles by implementing perturbations in initial conditions (ICs). This study investigates the impacts of various IC perturbation methods on convection-permitting ensemble forecasting over South China in the rainy season. Various IC perturbation methods show discrepant multiscale characteristics of perturbations, which generally complement each other when these perturbations are combined. The added values of combining various IC perturbation methods in forecasting are confirmed for most variables. However, such values are case dependent, with the largest values for tropical cyclone cases during the early forecasts and for the presummer rainy season cases with strong synoptic-scale forcing during late forecasts. Thus, it is still essential to further improve the combination of various types of IC perturbation methods.

Quantifying the impact of assimilating sensitive observations on heavy precipitation forecast with ensemble-based forecast sensitivity to observations technique

In this study, the Ensemble-based Forecast Sensitivity to Observations (EFSO) technique was used to examine the impact of assimilated sensitive observations on the forecast of heavy rainfall through three data assimilation strategies, i.e., running without observation assimilation (CNTL); with assimilation of all observations (CNTL_DA); with assimilation of sensitive observations (SEN_DA). The Infrared Atmospheric Sounder Interferometer presented the largest positive impact on the forecast, followed by the Advanced Microwave Sounding Unit A and surface land reports. The top 10% of observations contributing the largest positive impact on the forecast were located in the upstream ocean of the low-level winds associated with precipitation maximum. Compared to ensemble CNTL and CNTL_DA experiments, ensemble SEN_DA exhibited better precipitation probability, in which the representative member in ensemble SEN_DA successfully predicted the precipitation magnitude and location. Based upon the representative experiments of the ensemble CNTL, CNTL_DA, and SEN_DA, synoptic conditions were examined to understand how the SEN_DA improves the forecast of heavy precipitation. The three representative experiments exhibited similar characteristics and variations in large-scale circulation, horizontal water vapor transport, and wind fields. However, compared to the representative experiments of CNTL and CNTL_DA, the advection and barotropic components of potential divergence contributed to the increase and persistence of convective instability throughout heavy rainfall in SEN_DA. Heavy precipitation was closely associated with the intensity, duration, and location of the maximum convective instability. Furthermore, the release of convective instability depended on the mesoscale ascent, which determined the maximum and location of the precipitation. These results help to understand the optimization of observational design and assimilation strategy.

A novel rainfall forecast model using GNSS observations and CAPE

Precipitable water vapor (PWV) and zenith total delay (ZTD) are highly correlated indicators used for forecasting rainfall effectively. These two factors are widely used when establishing qualitative and quantitative rainfall forecast models. Another indicator, however, convective available potential energy (CAPE), is also highly correlated with the occurrence of rainfall, but has not been combined with PWV or ZTD in previous studies. Therefore, a novel rainfall forecast model based on support vector regression that combines CAPE and Global Navigation Satellite System (GNSS)-derived PWV is proposed in this paper. Moreover, annual, seasonal scales, and hourly autocorrelation for predictors were also considered, and wavelet coherence (WTC) was introduced to further reveal the correlation relationships between CAPE, PWV and rainfall in both time and frequency domains. Hourly PWV, CAPE, temperature (T) and rainfall over a time span of three years were collected from four stations in Singapore and Taiwan to evaluate the performance of the proposed model. The root mean square error (RMSE) and mean absolute error (MAE) between the measured and forecasted rainfall were calculated for each year and averaged. Subsequently, the average RMSE and MAE were determined for the annual seasonal rainfall. The annual average RMSE value was 0.71 mm and the average annual seasonal RMSE value was 0.69 mm. The annual average MAE value was 0.21 mm and the annual seasonal was 0.22 mm. The average correlation coefficient (R2) for the annual value was 0.94 and the annual seasonal R2 value was 0.93. These results indicate that the forecast accuracy at the seasonal scale is basically consistent with annual scale. Additionally, a measure called comparable RMSE (CRMS) was introduced to evaluate the forecasting accuracy across all grades of rainfall. This analysis showed that the accuracy of moderate rainfall (MR) and heavy rainfall (HR) forecasts is similar, but slight rainfall (SR) forecasts are the most accurate among seasons and schemes.

Heavy Rainfall Forecast of Landfalling Tropical Cyclone Over China With an Upgraded DSAEF_LTP Model

AbstractSince the release of the Dynamical‐Statistical‐Analog Ensemble Forecast for Landfalling Typhoon Precipitation (DSAEF_LTP) model, a series of upgrades has been made to improve its heavy rainfall forecast performance for different cases and regions of China. However, little effort has been given to systematic evaluation of its multi‐upgraded version with a large sample size, especially for all the coastal regions of China. This study evaluates the performance of Version 1.0 (V1.0) and its improved Version 1.1 (V1.1) of the DSAEF_LTP model in simulating heavy rainfall amounts associated with 76 landfalling tropical cyclones (LTCs) over China during 2004–2016. The optimized schemes from these simulations are then applied to the heavy rainfall forecasts of 37 LTCs during 2017–2020. To further improve the forecast performance of V1.1, a subregional re‐ensemble scheme, referred to as V1.1R, is introduced after examining its performances over three coastal subregions of China. A comparison of the forecast performances of V1.0, V1.1, V1.1R, and four operational numerical weather prediction (NWP) models for the 37 LTCs shows that the performance of V1.1 is much better than that of V1.0, with a 65% higher summed threat score (TS) for over 250‐mm (TS250) and 100‐mm (TS100) rainfall; and that V1.1R's TS100 and TS250 values are further improved by 8% and 20%, respectively, as compared to V1.1. These values are superior to those of the four operational NWP models. Additionally, case studies reveal that the track distributions and intensities of LTCs may significantly impact the forecast performance of heavy rainfall.

Direct Assimilation of Ground-Based Microwave Radiometer Clear-Sky Radiance Data and Its Impact on the Forecast of Heavy Rainfall

Ground-based microwave radiometer (GMWR) data with high spatial and temporal resolution can improve the accuracy of weather forecasts when effectively assimilated into numerical weather prediction. Nowadays, the major method to assimilate these data is via indirect assimilation by assimilating the retrieved profiles, which introduces large retrieval errors and cannot easily be represented by an error covariance matrix. Direct assimilation, on the other hand, can avoid this issue. In this study, the ground-based version of the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV-gb) was selected as the observation operator, and a direct assimilation module for GMWR radiance data was established in the Weather Research and Forecasting Model Data Assimilation (WRFDA). Then, this direct assimilation module was applied to assimilate GMWR data. The results were compared to the indirect assimilation experiment and demonstrated that direct assimilation can more effectively improve the model’s initial fields in terms of temperature and humidity than indirect assimilation while avoiding the influence of retrieval errors. In addition, direct assimilation performed better in the precipitation forecast than indirect assimilation, making the main precipitation center closer to the observation. In particular, the improvement in the precipitation forecast with a threshold of 60 mm/6 h was obvious, and the corresponding TS score was significantly enhanced.

Hybrid Dynamical–Statistical Forecasts of the Risk of Rainfall in Southeast Asia Dependent on Equatorial Waves

Abstract Equatorial waves are a major driver of widespread convection in Southeast Asia and the tropics more widely, a region in which accurate heavy rainfall forecasts are still a challenge. Conditioning rainfall over land on local equatorial wave phases finds that heavy rainfall can be between 2 and 4 times more likely to occur in Indonesia, Malaysia, Vietnam, and the Philippines. Equatorial waves are identified in a global numerical weather prediction ensemble forecast [Met Office Global and Regional Ensemble Prediction System (MOGREPS-G)]. Skill in the ensemble forecast of wave activity is highly dependent on region and time of year, although generally forecasts of equatorial Rossby waves and westward-moving mixed Rossby–gravity waves are substantially more skillful than for the eastward-moving Kelvin wave. The observed statistical relationship between wave phases and rainfall is combined with ensemble forecasts of dynamical wave fields to construct hybrid dynamical–statistical forecasts of rainfall probability using a Bayesian approach. The Brier skill score is used to assess the skill of forecasts of rainfall probability. Skill in the hybrid forecasts can exceed that of probabilistic rainfall forecasts taken directly from MOGREPS-G and can be linked to both the skill in forecasts of wave activity and the relationship between equatorial waves and heavy rainfall in the relevant region. The results show that there is potential for improvements of forecasts of high-impact weather using this method as forecasts of large-scale waves improve.

Combined assimilation of hourly rainfall data and every 10-min AHI radiance with WRF 4DVar for the short-range heavy rainfall forecast in Eastern China

Rainfall observations and geostationary satellite water vapor radiances account for complementary humidity information over rainy areas and large-scale environments, respectively. This study investigates the simultaneous assimilation of China Hourly Merged Precipitation Analysis (CHMPA) data and clear-sky water vapor radiances provided by the Himawari-8 Advanced Himawari Imager (AHI) with Four-Dimensional Variational (4DVar) data assimilation method using Weather Research and Forecasting (WRF) model with an hourly rapid-update cycling data assimilation configuration. The rapid-update 4DVar assimilation is configured as 10-min interval for AHI radiances and 1-h interval for CHMPA rainfall data in a same 1-h assimilation time window. Generally positive impacts from assimilating CHMPA rainfall and AHI water vapor radiance observations are achieved for the 12-h short-term precipitation forecast. Error reductions are found after assimilating CHMPA rainfall and AHI water vapor radiance observations for both analyses and forecasts when compared to ERA5 reanalysis. It is found that combined assimilation of CHMPA rainfall and AHI water vapor radiance data has clearly positive impacts on heavy rainfall forecasts. A heavy rainfall case that occurred in Jiangsu Province on 28 June 2020 is examined in details. The combined assimilation of infrared radiances from AHI water vapor channels together with CHMPA rainfall data improves the vertical and horizontal transport of humidity conditions, which better adjusts the humidity distribution. The over-estimation of rainfall forecast caused by the assimilation of CHMPA is alleviated. This result can provide reference for quantitative precipitation forecasts of regional numerical weather model.

Short to medium range impact based forecasting of heavy rainfall in India

There have been major advances in the last few decades in our understanding of heavy rainfall during monsoon season due to substantial progress in both observation and numerical modelling of monsoon. All these resulted in more accurate forecast of heavy rainfall in short to medium range, (upto five days) with 40% improvement in accuracy of heavy rainfall forecast in recent five years (2018-2022) as compared to previous five years. However, improvement of forecast and warning skill is not sufficient to minimize damage to lives and property. It is essential to extend to hazard forecast systems (hazard models) and then to impact and risk assessment with stakeholder interaction for risk based warning (RBW) and response action to protect lives and livelihoods Considering all these, India Meteorological Department (IMD) has introduced impact based forecast (IBF) for heavy rainfall at meteorological sub-division level since July 2013 and at district and city scale in August, 2019 in its short to medium range forecasts and nowcasts indicating the likely impact of the heavy rainfall in different sectors and required response actions. Thereafter the IBF of heavy rainfall has undergone several changes over the years. Currently, the IBF being implemented by IMD includes all the four components, viz., (i) meteorological hazards, (ii) geophysical hazards, (iii) geospatial applications and (iv) socio-economic conditions and it utilises a web-GIS based decision support system (DSS). In this study we have reviewed various approaches and stages of development of IBF of heavy rainfall in India. The success of IBF of heavy rainfall will enhance the management of critical resources like agriculture, water & power and support urban and disaster management sectors among others while reducing loss of life and property.

A multi-model ensemble tool for predicting districts level monsoon rainfall and extreme rainfall events over India

The southwest (SW) monsoon season from June to September (JJAS) is the major rainfall period over most parts of Indian regions. Accurate rainfall forecast is one of the most crucial and least predictable parameters of the numerical weather prediction (NWP) models because of its uneven distribution and patterns over the globe. During the last decade many studies have been carried out using different NWP models to predict rainfall incidents, and it is found that the forecast skill has been improved considerably. In the present study, a multi-model ensemble (MME) based tool has been developed for the prediction of SW monsoon rainfall at the district level over India for five days. The precipitation forecasts from five operational NWP modelling systems, viz., (i) Global Forecast System (GFS) and (ii) Global Ensemble Forecasting System (GEFS) running at India Meteorological Department, (iii) Global Forecast System model running at National Centre for Environment Prediction (NCEP), (iv) Unified Model running at National Centre for Medium-Range Weather Forecasting (NCMRWF) and (v) Global Spectral Model (GSM) running at Japan Meteorological Agency (JMA) have been used for developing the MME forecasts for SW monsoon 2021. The prediction skill of the MME and the individual model forecast is evaluated against observed district rainfall. The district-level heavy rainfall forecast from individual models and MME is also evaluated against the observed rainfall events useful for warning services. Different verification scores like Correlation Coefficient (CC), Root Mean Square Error (RMSE), Mean Bias, Probability of Detection (POD), False Alarm Ratio (FAR), Equitable Treat Score (ETS), Critical Success Index (CSI), etc. are calculated for the verification purpose. The different verification score shows that MME rainfall forecast has performed well than the individual models in different spatial domains and temporal scales. The CC between observed rainfall and day 1 MME forecast is 0.58, whereas GFS, GEFS, NCEP, NCUM and JMA are showing 0.43, 0.47, 0.49, 0.49 and 0.46 respectively. The RMSE observed for MME, GFS, GEFS, NCEP, NCUM and JMA are 12.7, 15.2, 14.1, 14.3, 16.6 and 14.1 mm/day respectively when compared with IMD observed rainfall. The inter-comparison of the model forecasts reveal that the MME method can generate skillful district rainfall forecast over India for operational use during the monsoon season.

Weather patterns in Southeast Asia: Enhancing high‐impact weather subseasonal forecast skill

AbstractWhile skilful forecasts of heavy rainfall are highly desirable for weather warnings and mitigating impacts, forecasting such events is notoriously difficult, even with the most advanced numerical weather prediction models, due to the strong dependence on convective‐scale processes. The large‐scale circulation, on the other hand, is typically more predictable. Weather patterns (WPs) are a set of circulation types obtained statistically that can be used to characterize regional weather and harness the predictability of the large‐scale circulation. In this work we produce pattern‐conditioned probabilistic rainfall forecasts by projecting the horizontal winds from the Met Office GloSea5 prediction system on to WPs and then using the observed relationship between each WP and rainfall estimated by satellite. The WPs are derived following a novel two‐tier clustering technique: the WPs in the first tier represent planetary‐scale variability, such as El Niño–Southern Oscillation (ENSO), while the WPs in the second tier capture synoptic‐scale variability. We investigate WP predictability as well as the improvement in skill of subseasonal rainfall forecasts gained by this technique. GloSea5 predicts the WP occurrence with skill extending beyond lead day 10. The pattern‐conditioned rainfall forecasts were evaluated against climatological forecasts and model‐simulated rainfall hindcasts. We show that the pattern‐conditioned forecasts are skilful and outperform the model‐simulated rainfall hindcasts for lead times extending to days 10–20, depending on the specific exceedance criteria and region. Spatial aggregation leads to increased levels of skill, but not to a significant extension of the skilful prediction horizon. These results constitute a fundamental step for the development of subseasonal prediction systems for Southeast Asia.

Improving precipitation ensemble forecasts of typhoon heavy rainfall over East China with a modified probability-matching technique

Improving precipitation ensemble forecasts of typhoon heavy rainfall over East China with a modified probability-matching technique

Forecasts of the Warm-Sector Heavy Rainfall With a Warm Shear Pattern Over Coastal Areas of the Yangtze–Huaihe River in a Regional Business Forecast Model

Based on the observational hourly precipitation data and the ERA5 reanalysis datasets, the short-term forecasts of the warm-sector heavy rainfall with warm-type shear line (WRWS) events over the coastal areas of the Yangtze–Huaihe River (YHR) are investigated in the regional business model Precision Weather Analysis and Forecasting System (PWAFS). Evaluations and diagnoses are carried out via objective estimations and composition analyses for the rainy season of 2017. Results show that the short-term forecasts of PWAFS are characterized by considerable skills for WRWS events in the coastal areas of YHR in view of the object-based diagnostic evaluation, which, however, tend to generate the rain belts with northeast shift phases and weaker intensities. Meantime, the threat score results for WRWS-associated processes show that the model forecasting skill declines sharply as the precipitation intensity increases. Moreover, composition differences of the synoptic-scale thermodynamic characteristics between observations and forecast results are diagnosed to reveal the possible mechanisms of the short-term forecast biases toward WRWS. The zonal westerlies are overestimated in the model, while the southerlies are underestimated in the lower troposphere over coastal areas of YHR, leading to the northeastward shifted shear line and the absent moisture channel associated with the East China Sea at the boundary layer. Attributed to these atmospheric circulation biases, the accumulated warm and moist energy is weaker at the boundary layer, and hence, the short-term forecasts of the rain-belt location for WRWS over the YHR coastal areas have northeast shifting phases with weaker intensities of precipitation in forecasts of the regional business model PWAFS.

Skillful Long‐Lead Prediction of Summertime Heavy Rainfall in the US Midwest From Sea Surface Salinity

AbstractSummertime heavy rainfall and its resultant floods are among the most harmful natural hazards in the US Midwest, one of the world's primary crop production areas. However, seasonal forecasts of heavy rain, currently based on preseason sea surface temperature anomalies (SSTAs), remain unsatisfactory. Here, we present evidence that sea surface salinity anomalies (SSSAs) over the tropical western Pacific and subtropical North Atlantic are skillful predictors of summer time heavy rainfall one season ahead. A one standard deviation change in tropical western Pacific SSSA is associated with a 1.8 mm day−1 increase in local precipitation, which excites a teleconnection pattern to extratropical North Pacific. Via extratropical air‐sea interaction and long memory of midlatitude SSTA, a wave train favorable for US Midwest heavy rain is induced. Combined with soil moisture feedbacks bridging the springtime North Atlantic salinity, the SSSA‐based statistical prediction model improves Midwest heavy rainfall forecasts by 92%, complementing existing SSTA‐based frameworks.